An overview of various propagation model for mobile communication

Mollel, Michael S.; Kisangiri, Michael

doi:10.1109/scat.2014.7055150

Cited by 21 publications

(23 citation statements)

References 7 publications

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“…The Okumura–Hata Urban Path Loss model is an empirical model that is often used in sub-GHz wireless communication systems. While the COST-231 urban model extended its use up to 2 GHz, the Electronic Communication Committee (ECC) modified the original Okumura–Hata model to work with frequencies up to (and beyond) 3 GHz in the ECC-33 model [ 30 , 31 ]. Therefore, the ECC-33 model is suitable to evaluate the maximum communication range of LoRa at

GHz in an urban environment.…”

Section: Path Loss Modelingmentioning

confidence: 99%

LoRa 2.4 GHz Communication Link and Range

Janssen

BniLam

Aernouts

et al. 2020

Sensors

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Recently, Semtech has released a Long Range (LoRa) chipset which operates at the globally available 2.4 frequency band, on top of the existing sub-GHz, km-range offer, enabling hardware manufacturers to design region-independent chipsets. The SX1280 LoRa module promises an ultra-long communication range while withstanding heavy interference in this widely used band. In this paper, we first provide a mathematical description of the physical layer of LoRa in the 2.4 band. Secondly, we investigate the maximum communication range of this technology in three different scenarios. Free space, indoor and urban path loss models are used to simulate the propagation of the 2.4 LoRa modulated signal at different spreading factors and bandwidths. Additionally, we investigate the corresponding data rates. The results show a maximum range of 333km in free space, 107m in an indoor office-like environment and 867m in an outdoor urban context. While a maximum data rate of 253.91 can be achieved, the data rate at the longest possible range in every scenario equals 0.595. Due to the configurable bandwidth and lower data rates, LoRa outperforms other technologies in the 2.4 band in terms of communication range. In addition, both communication and localization applications deployed in private LoRa networks can benefit from the increased bandwidth and localization accuracy of this system when compared to public sub-GHz networks.

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GHz in an urban environment.…”

Section: Path Loss Modelingmentioning

confidence: 99%

LoRa 2.4 GHz Communication Link and Range

Janssen

BniLam

Aernouts

et al. 2020

Sensors

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“…The footprint module, where the BS cells coverage area is estimated, is based on typical cell fingerprinting techniques based on propagation modelling [9]. However, a calibration method is used to potentiate the accuracy of the coverage maps, combining the model with real network measurements.…”

Section: B Footprint Modulementioning

confidence: 99%

“…1) Model Calibration: After calculating the cell footprints, a calibration process is applied to the results based on DT data, when available. Otherwise, typical values based on the existing propagation model for the path loss independent factors are provided [9]. When DTs data is available in the bins of interest, information of average received power is obtained, which allows to optimize path loss values based on DTs.…”

Section: B Footprint Modulementioning

confidence: 99%

Developing a LTE Localization Framework using Real Network Data towards RAN Optimization through Context Knowledge

Borralho

Duarte

Quddus

et al. 2020

2020 23rd International Symposium on Wireless Personal Multimedia Communications (WPMC)

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The exponential growth of the network elements and data traffic exchange in the last few years elevated the need of network providers for optimized and cost-efficient solutions regarding network management and monitorization. Solutions such as drive-tests (DTs) are becoming extremely expensive with the vast extension and complexity of nowadays mobile networks. Therefore, this paper provides a solution for optimized networkcontext knowledge acquisition, towards the self-organizing networks (SONs) concept. The presented framework incorporates an entire scheme for network Traces processing and positioning, based on network measurements and fingerprinting techniques. This framework enables a series of different use cases for network management and optimization, with real-time data processing capabilities within the network Traces collection interval (15 minutes), and achieving a median positioning error of 90 m.

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“…Mollel and Kisangiri [7] elaborates various propagation model. The present paper gives emphasis to statistical models and explains models limitations and applications.…”

Section: Related Workmentioning

confidence: 99%

Effect of Different Propagation Models in Routing Protocols

Khan¹,

Das²

2019

IJEAT

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Mobile Ad hoc Networks (MANET) are wireless networks where communication of nodes takes placevia radio waves. Due to dynamic topology and mobility of nodes frequent path failure takes place which in return affects the Quality of Service (QoS) in MANET. This paper mainly focuses on the experimental analysis on different propagation models namely Two-ray ground reflection, Free Space and Shadowing models on AODV and DSDV. We have done rigorous experiments to verify the effects of various propagation models and try to find its environment suitability. The QoS parameters we have used for the observation of the performance are throughput, delay and Packet-Delivery-Ratio (PDR). Simulation is done using NS-2.Free Space model gives better performance in both the protocols in contrast to other models. This paper will be helpful for researchers, students who are newly involved in research for better understanding and utilization of propagation models in corresponding environment.

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An overview of various propagation model for mobile communication

Cited by 21 publications

References 7 publications

LoRa 2.4 GHz Communication Link and Range

LoRa 2.4 GHz Communication Link and Range

Developing a LTE Localization Framework using Real Network Data towards RAN Optimization through Context Knowledge

Effect of Different Propagation Models in Routing Protocols

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